require(stats); require(graphics)
asc <- anscombe
asc

##-- now some "magic" to do the 4 regressions in a loop:
ff <- y ~ x
mods <- setNames(as.list(1:4), paste0("lm", 1:4))
for(i in 1:4) {
  ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
  ## or   ff[[2]] <- as.name(paste0("y", i))
  ##      ff[[3]] <- as.name(paste0("x", i))
  mods[[i]] <- lmi <- lm(ff, data = anscombe)
  print(anova(lmi))
}

## See how close they are (numerically!)
sapply(mods, coef)

lapply(mods, function(fm) coef(summary(fm)))

## Now, do what you should have done in the first place: PLOTS
op <- par(mfrow = c(2, 2), mar = 0.1+c(4,4,1,1), oma =  c(0, 0, 2, 0))
for(i in 1:4) {
  ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
  plot(ff, data = anscombe, col = "red", pch = 21, bg = "orange", cex = 1.2,
       xlim = c(3, 19), ylim = c(3, 13))
  abline(mods[[i]], col = "blue")
}
mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex = 1.5)

library(datasauRus)
datasaurus_dozen

if(requireNamespace("dplyr")){
  suppressPackageStartupMessages(library(dplyr))
  datasaurus_dozen %>% 
    group_by(dataset) %>% 
    summarize(
      mean_x    = mean(x),
      mean_y    = mean(y),
      std_dev_x = sd(x),
      std_dev_y = sd(y),
      corr_x_y  = cor(x, y)
    )
}

if(requireNamespace("ggplot2")){
  library(ggplot2)
  ggplot(datasaurus_dozen, aes(x=x, y=y, colour=dataset))+
    geom_point()+
    theme_void()+
    theme(legend.position = "none")+
    facet_wrap(~dataset, ncol=3)
}

plot(weather$temp)

hist(weather$temp,
     xlab = 'Температура', ylab = 'Частота', main = 'Распределение температуры в Украине')

hist(weather$temp,  breaks = 60,
     xlab = 'Температура', ylab = 'Частота', main = 'Распределение температуры в Украине')

hist(weather$temp,  breaks = 60, col = "lightblue", border = "pink",
     xlab = 'Температура', ylab = 'Частота', main = 'Распределение температуры в Украине')

hist(weather$temp,  breaks = 60, col = "lightblue", border = "pink",
     freq = FALSE,
     xlab = 'Температура', ylab = 'Частота', main = 'Распределение температуры в Украине')

hist(weather$temp,  breaks = 30, col = "lightblue", border = "pink",
     freq = FALSE,
     xlab = 'Температура', ylab = 'Частота', main = 'Распределение температуры в Украине')
lines(density(weather$temp), col = "red", lwd = 2)

boxplot(temp ~ city_name, data = weather)

boxplot(temp ~ city_name, data = weather,
         xlab = "Города",
            ylab = "Температура",
            main = "Температура по городам Украины",
            col = "coral")

boxplot(temp ~ city_name, data = weather,
         xlab = "Температура",
            ylab = "Города",
            main = "Температура по городам Украины",
            col = c('chartreuse3', 'red' , 'cyan', 'darkorange1'),
            horizontal = TRUE)
grid()

ggplot(data = weather) +
  geom_point(mapping = aes(x = temp, y = pressure))

ggplot(data = oneMonthKiev) +
  geom_point(mapping = aes(x = temp, y = pressure))

ggplot(data = weather) +
  geom_point(mapping = aes(x = temp, y = pressure, 
                           color = clouds_all))

ggplot(data = oneMonthKiev) +
  geom_point(mapping = aes(x = temp, y = pressure,
                           color = weather_main,
                           #shape = weather_main,
                           size = wind_speed)) +
  labs(
    title = "Погода в Киеве",
    subtitle = "Март 2018",
    caption = "По данным: https://openweathermap.org/",
    x = "Температура",
    y = "Атмосферное давление",
    color = "Погода",
    size = "Сила ветра"
  )

ggplot(data = weather) +
  geom_point(mapping = aes(x = temp, y = pressure,
                           color = weather_main,
                           size = wind_speed))+
  facet_wrap(~weather_main, nrow = 3)

ggplot(data = weather) +
  geom_point(mapping = aes(x = temp, y = pressure,
                           color = weather_main))+
  facet_grid(city_name~weather_main)

ggplot(data = weather) +
  geom_point(mapping = aes(x = temp, y = pressure,
                           color = weather_main))+
  facet_grid(.~city_name)

ggplot(data = weather,aes(x = temp, y = pressure)) +
  geom_smooth()

ggplot(data = weather) +
  geom_smooth(mapping = aes(x = temp, y = pressure,
                            linetype = city_name,
                            color = city_name))

ggplot(data = oneMonthKiev) +
  geom_point(mapping = aes(x = temp, y = pressure)) +
  geom_smooth(mapping = aes(x = temp, y = pressure))

ggplot(data = oneMonthKiev, mapping = aes(x = temp, y = pressure)) +
  geom_point() +
  geom_smooth()

ggplot(data = oneMonthKiev, mapping = aes(x = temp, y = pressure)) +
  geom_point(mapping = aes(color = weather_main)) +
  geom_smooth()

oneMonthKiev %>%
ggplot(aes(x = temp, y = pressure)) +
  geom_point(mapping = aes(color = weather_main)) +
  geom_smooth(data = filter(oneMonthKiev, weather_main == 'Rain'),
              se = F
  )

ggplot(data = weather, mapping = aes(weather_main)) +
  geom_bar()

ggplot(data = weather, mapping = aes(weather_main)) +
  stat_count()

ggplot(data = weather) +
  geom_bar(mapping = aes(x = weather_main, y = ..prop.., group = 1))

ggplot(data = weather, mapping = aes(temp)) +
  geom_histogram(binwidth = 1) +
  theme_bw()

ggplot(data = weather, mapping = aes(weather$temp)) +
  stat_bin(binwidth = 1)

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, color = weather_main))

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, fill = weather_main))

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, fill = city_name))

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, fill = city_name),
           position = 'fill'
           )

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, fill = city_name),
           position = 'dodge'
           )

ggplot(data = oneMonthKiev) +
  geom_point(mapping = aes(x = temp, y = pressure)
)

ggplot(data = oneMonthKiev) +
  geom_point(mapping = aes(x = temp, y = pressure),
            position = 'jitter'
)

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, fill = city_name)) +
  coord_flip()

ggplot(data = weather,mapping = aes(x = city_name,y = temp, fill = city_name)) +
  geom_boxplot() +
  coord_flip()

ggplot(data = weather) +
  geom_bar(mapping = aes(weather_main, fill = city_name)) +
  coord_polar()

#install.packages('plotly', dependencies = T)
library(plotly)

plot_ly(weather, y = ~temp,color = ~year, type = "box")

weather %>%
  plot_ly(
    x = ~year,
    y = ~temp,
    split = ~year,
    type = 'violin',
    box = list(
      visible = T
    ),
    meanline = list(
      visible = T
    )
  ) %>% 
  layout(
    xaxis = list(
      title = "Year"
    ),
    yaxis = list(
      title = "Temp",
      zeroline = F
    )
  )

plot_ly(oneMonthKiev, x = ~temp, y = ~pressure, z = ~humidity, color = ~weather_main) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Temp'),
                     yaxis = list(title = 'Pressure'),
                     zaxis = list(title = 'Humidity')))

gp <- ggplot(data = weather,mapping = aes(x = temp, y = pressure,
                                linetype = city_name,
                                color = city_name)) +
        geom_smooth()

ggplotly(gp)